Chotirat Ann Ratanamahatana

7.0k citations

79 papers · 5.3k · 2 hit papers · h-index 27

Impact in

Signal Processing top 0.1%
- Time Series Analysis and Forecasting
- Music and Audio Processing
- Data Management and Algorithms
Artificial Intelligence top 0.5%
- Anomaly Detection Techniques and Applications
- Advanced Text Analysis Techniques

Papers in

Signal Processing 63
- Time Series Analysis and Forecasting 58
- Music and Audio Processing 28
- Data Management and Algorithms 20
Artificial Intelligence 45
- Anomaly Detection Techniques and Applications 24
- Advanced Text Analysis Techniques 9

Co-authors: Eamonn Keogh (22 shared papers)Vit Niennattrakul (21 shared papers)Stefano Lonardi (5 shared papers)Wei Li (5 shared papers)Xiaopeng Xi (1 shared paper)Christian R. Shelton (1 shared paper)Dimitrios Gunopulos (3 shared papers)John F. Pane (1 shared paper)
Journals: Data Mining and Knowledge Discovery (3 papers)Knowledge-Based Systems (2 papers)Lecture notes in computer science (15 papers)International Journal of Human-Computer Studies (1 paper)Emerging Science Journal (1 paper)
Partner nations: Thailand United States Hong Kong

In The Last Decade

Chotirat Ann Ratanamahatana

79 papers receiving 5.0k citations

Chotirat Ann Ratanamahatana's Hit Papers

align trajectories log scale

Fast time series classification using numerosity reduction 2006 · 490 citations

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if any of the following hold:

it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.

2006 Fast time series classification using numerosity reduction
2004 Knowledge and Information Systems

Peers

Countries citing papers authored by Chotirat Ann Ratanamahatana

Since Specialization

Citations

This map shows the geographic impact of Chotirat Ann Ratanamahatana's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Chotirat Ann Ratanamahatana with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chotirat Ann Ratanamahatana more than expected).

Fields of papers citing papers by Chotirat Ann Ratanamahatana

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chotirat Ann Ratanamahatana. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Chotirat Ann Ratanamahatana. The network helps show where Chotirat Ann Ratanamahatana may publish in the future.

Co-authors

The 25 scholars most cited alongside Chotirat Ann Ratanamahatana, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Chotirat Ann Ratanamahatana Line = papers co-authored together Chotirat Ann Ratanamahatana links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

Showing the 20 most-cited of 79 papers — load more, or switch the sort, to bring in the rest.

#	Work
1	Exact indexing of dynamic time warping Knowledge and Information Systems ·Eamonn Keogh, Chotirat Ann Ratanamahatana Hit paper breakdown →	2004	1491
2	Fast time series classification using numerosity reduction Xiaopeng Xi, Eamonn Keogh, Christian R. Shelton, Wei Li, Chotirat Ann Ratanamahatana Hit paper breakdown →	2006	490
3	Towards parameter-free data mining Eamonn Keogh, Stefano Lonardi, Chotirat Ann Ratanamahatana	2004	447
4	Three Myths about Dynamic Time Warping Data Mining Chotirat Ann Ratanamahatana, Eamonn Keogh	2005	305
5	Making Time-series Classification More Accurate Using Learned Constraints Chotirat Ann Ratanamahatana, Eamonn Keogh	2004	304
6	Everything you know about Dynamic Time Warping is Wrong Chotirat Ann Ratanamahatana, Eamonn Keogh	2004	227
7	Studying the language and structure in non-programmers' solutions to programming problems International Journal of Human-Computer Studies ·John F. Pane, Chotirat Ann Ratanamahatana, Brad A. Myers	2001	225
8	Scaling and time warping in time series querying The VLDB Journal ·Ada Wai-Chee Fu, Eamonn Keogh, Leo Yung Hang Lau, Chotirat Ann Ratanamahatana, Raymond Chi-Wing Wong	2007	170
9	On Clustering Multimedia Time Series Data Using K-Means and Dynamic Time Warping Vit Niennattrakul, Chotirat Ann Ratanamahatana	2007	142
10	Time-series Bitmaps: a Practical Visualization Tool for Working with Large Time Series Databases Nitin Kumar, Venkata Surya Sai Charan Lolla, Eamonn Keogh, (unknown), Chotirat Ann Ratanamahatana, Wei Li	2005	120
11	Feature selection for the naive bayesian classifier using decision trees Applied Artificial Intelligence ·Chotirat Ann Ratanamahatana, Dimitrios Gunopulos	2003	111
12	Mining Time Series Data Chotirat Ann Ratanamahatana, Jessica Lin, Dimitrios Gunopulos, Eamonn Keogh, Michail Vlachos, Gautam Das	2009	96
13	A Novel Bit Level Time Series Representation with Implication of Similarity Search and Clustering Lecture notes in computer science ·Chotirat Ann Ratanamahatana, Eamonn Keogh, Anthony Bagnall, Stefano Lonardi	2005	82
14	Assumption-free anomaly detection in time series Wei Li, Nitin Kumar, Venkata Surya Sai Charan Lolla, Eamonn Keogh, Stefano Lonardi, Chotirat Ann Ratanamahatana	2005	79
15	Compression-based data mining of sequential data Data Mining and Knowledge Discovery ·Eamonn Keogh, Stefano Lonardi, Chotirat Ann Ratanamahatana, Wei Li, Sang‐Hee Lee, John C. Handley	2007	76
16	Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery Computational and Mathematical Methods in Medicine ·Haemwaan Sivaraks, Chotirat Ann Ratanamahatana	2015	60
17	A Bit Level Representation for Time Series Data Mining with Shape Based Similarity Data Mining and Knowledge Discovery ·Anthony Bagnall, Chotirat Ann Ratanamahatana, Eamonn Keogh, Stefano Lonardi, G. J. Janacek	2006	59
18	Shape averaging under Time Warping Vit Niennattrakul, Chotirat Ann Ratanamahatana	2009	45
19	Inaccuracies of Shape Averaging Method Using Dynamic Time Warping for Time Series Data Lecture notes in computer science ·Vit Niennattrakul, Chotirat Ann Ratanamahatana	2007	44
20	Shape-Based Clustering for Time Series Data Lecture notes in computer science ·Warissara Meesrikamolkul, Vit Niennattrakul, Chotirat Ann Ratanamahatana	2012	42

About Chotirat Ann Ratanamahatana

Chotirat Ann Ratanamahatana is a scholar working on Signal Processing, Artificial Intelligence, Computer Vision and Pattern Recognition, Economics and Econometrics and Information Systems, having authored 79 papers that have together received 5.3k indexed citations. Recurring topics across this work include Time Series Analysis and Forecasting (58 papers), Music and Audio Processing (28 papers), Anomaly Detection Techniques and Applications (24 papers), Data Management and Algorithms (20 papers), Complex Systems and Time Series Analysis (9 papers), Advanced Text Analysis Techniques (9 papers), Video Analysis and Summarization (6 papers) and Advanced Chemical Sensor Technologies (5 papers). The work is most often cited by research in Signal Processing (3.4k citations), Artificial Intelligence (2.6k citations), Computer Vision and Pattern Recognition (910 citations), Human-Computer Interaction (192 citations) and Economics and Econometrics (671 citations). Chotirat Ann Ratanamahatana has collaborated with scholars based in Thailand, United States and Hong Kong. Frequent co-authors include Eamonn Keogh, Vit Niennattrakul, Stefano Lonardi, Wei Li, Xiaopeng Xi, Christian R. Shelton, Dimitrios Gunopulos, John F. Pane, Brad A. Myers and Ada Wai-Chee Fu. Their work appears in journals such as Data Mining and Knowledge Discovery, Knowledge-Based Systems, Lecture notes in computer science, International Journal of Human-Computer Studies and Emerging Science Journal.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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Chotirat Ann Ratanamahatana

Chotirat Ann Ratanamahatana's Hit Papers

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About Chotirat Ann Ratanamahatana

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